Book Description

Haskell is trending in the field of data science by providing a powerful platform for robust data science practices. This book provides you with the skills to handle large amounts of data, even if that data is in a less than perfect state. Each chapter in the book helps to build a small library of code that will be used to solve a problem for that chapter. The book starts with creating databases out of existing datasets, cleaning that data, and interacting with databases within Haskell in order to produce charts for publications. It then moves towards more theoretical concepts that are fundamental to introductory data analysis, but in a context of a real-world problem with real-world data. As you progress in the book, you will be relying on code from previous chapters in order to help create new solutions quickly. By the end of the book, you will be able to manipulate, find, and analyze large and small sets of data using your own Haskell libraries.

What You Will Learn

Learn the essential tools of Haskell needed to handle large data

Migrate your data to a database and learn to interact with your data quickly

Clean data with the power of Regular Expressions

Plot data with the Gnuplot tool and the EasyPlot library

Formulate a hypothesis test to evaluate the significance of your data

Evaluate the variance between columns of data using a correlation statistic and perform regression analysis

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